Claude Opus 5.5 vs GPT-6.1 Sol: which should you use for coding and agents?

The short answer

If your priority is the highest public coding point estimate and you can afford the higher per-token rates, start with Claude Opus 5.5. If your priority is lower cost per task or fitting very long context into one request, start with GPT-6.1 Sol. For multi-step agent work, neither vendor-neutral leaderboard in this evidence set names a winner, so treat agent claims as unproven until you test them in your own harness.

This is analysis on top of the facts below, not a vendor verdict. Both models will change, and your editor, agent scaffold, and effort settings move results more than most people expect.

What the shared benchmarks actually support

The following figures come from the BenchLM comparison page as fetched on October 7, 2026. BenchLM is a secondary aggregator, not a primary test lab, and it labels each row with an evidence basis. Like-for-like means both scores rest on Supported evidence or the same weighted set; directional means they do not, and the page does not name a winner there.

• Overall point estimates on the BenchLM page: 86.39 for Opus 5.5 against 81.46 for GPT-6.1 Sol, with overlapping conditional ranges that the page says do not establish rank confidence.
• Shared evidence is thin: 9 shared results, 44 Opus-5.5-only results, 3 Sol-only results, and only 2 of 8 category rows rated like-for-like.
• Coding lane (BenchAlign v5.8, like-for-like): Opus 5.5 83.5 at rank 2 of 144 against Sol 71.0 at rank 8 of 144, with 10 public rows against 1. The page reading is Opus 5.5 leads with overlapping intervals.
• Knowledge lane (like-for-like): Opus 5.5 88.3 at rank 1 of 173 against Sol 82.7 at rank 4 of 173.
• Agentic lane: Opus 5.5 88.5 on Supported evidence against Sol 72.1 on Estimated evidence. The page marks this directional only and names no winner.
• Two shared benchmark rows shown on the page: AutomationBench Agentic at 40.0 percent for Opus 5.5 against 36.1 percent for Sol, and ARC-AGI-2 Reasoning at 91.7 percent for Opus 5.5 against 94.2 percent for Sol, where Sol is higher. Each row cites vendor or prize sources, and sparse rows are shown as a list rather than closed into a shape.

What each workload costs at listed rates

On all three fixed token mixes the page models, GPT-6.1 Sol has the lower estimated cost. The confidence label on these rows is listed-rates, which means the arithmetic follows published per-token prices rather than measured spend on your workload. Caching, retries, reasoning effort, and agent loops change real spend substantially.

• Chat turn of 1,000 fresh input plus 500 output tokens: modeled at 0.014 dollars for Opus 5.5 against 0.007 dollars for Sol.
• Repository review of 50,000 fresh input plus 3,000 output tokens: modeled at 0.26 dollars against 0.13 dollars.
• Cache-heavy agent loop of 200,000 cached plus 20,000 fresh input plus 10,000 output tokens: modeled at 0.32 dollars against 0.16 dollars.
• The page states costs use listed standard API rates and notes where cached input falls back to list rates. Treat these as planning mixes, not your bill.

Context, access, and where each one fits

For long documents the BenchLM page favors GPT-6.1 Sol on the documented-context ground: it lists Sol as having the larger documented context window, with documented confidence. It does not present this as a quality claim, only a fit claim for prompts that approach the limit.

For interactive coding in a terminal-first agent, sibling evidence gives Opus-side context but not a Sol verdict. A September 23, 2026 Vellum comparison of Opus 5.5 against GPT-6 Astra, a different OpenAI flagship, reports vendor-announcement-table figures of 66.4 percent for Opus 5.5 against 57.7 percent for Astra on Terminal-Bench 4.0, with Opus 5.5 at 4 dollars in and 20 dollars out per million tokens against Astra at 10 and 50. That supports Opus 5.5 as a strong terminal-coding model in its generation, but Astra is not Sol, so do not carry the Astra gap across to Sol.

For harness context, a Morph table verified August 21, 2026 reports Terminal-Bench 2.1 harness scores for the prior generation at 89.5 percent for GPT-5.6 Sol with Codex and 89.1 percent for Opus 5 with Claude Code. Those are prior models on an older harness version, so they describe how the harness works and how close prior flagships were, not how Opus 5.5 or GPT-6.1 Sol score today.

How to choose in under a week

Pick the constraint first, then the model. If the constraint is coding accuracy per task and the budget allows higher per-token rates, trial Opus 5.5 first. If the constraint is per-task economy, high cache reuse, or very long prompts, trial Sol first. If the constraint is autonomous multi-step agent work, trial both, because the public evidence set here is directional only for agents.

Run the same three tasks in both models inside the same agent and editor: one bug fix, one multi-file refactor, and one long-context review. Keep effort settings fixed, record pass rate, steps taken, tokens used, and actual billed cost, then keep the winner for that workflow. Do not generalize from chat demos to agent loops.

• Your agent scaffold decides more than the model badge. The same model scores differently inside different agents, which is why Terminal-Bench reports agent-plus-model pairs rather than model-only scores.
• Reasoning effort and retries multiply cost. A cheaper per-token model run at high effort with many retries can cost more per finished task than a pricier model run once at medium effort.
• Long context is not free understanding. A larger window helps fit a repository slice or long document, but retrieval quality, instruction following, and tool-use discipline still decide whether the extra context helps.
• Vendor benchmarks are self-reported claims with stated methods, not independent verification. The ARC-AGI-2 row where Sol leads and the coding lane where Opus leads can both be true without settling which model writes better code in your repository.

Limits of this answer

This answer rests on a secondary aggregator page plus two secondary context pages, not on fresh independent testing by RepoRadar. BenchLM labels much of the Sol evidence Estimated and much of the matchup directional, and its intervals overlap. Prices are listed standard rates and move. Model behavior, retirement notices, and harness versions also move; the BenchLM page itself invites readers to follow model changes for price, version, and retirement notices.

RepoRadar did not hands-on test either model for this article. Treat the decision rule above as a testing plan, not a test result.

A useful next action

Set a one-week trial with a fixed budget cap and the three-task test above. Keep whichever model finishes more of your tasks at an acceptable cost inside your actual agent, not whichever model leads a public point estimate.

For background on model choice generally, read the related answers on which LLM to use for coding, what moving to Opus 5.5 changes, and how the GPT-6 Sol and Luna tiers differ.